Intrusion Detection Outline
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Nov 24, 2024
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Research Paper Outline: Intrusion Detection Using Machine Learning
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Research Paper Outline: Intrusion Detection Using Machine Learning
i)Abstract
The necessity for better Intrusion Detection Systems (IDS) is stronger than before due to
the overall increase in cybercrime.
Regarding early identification of intrusions in cases of intrusion detection inside the
network, machine learning (ML) approaches are crucial.
However, choosing the best approach is a difficult issue because there are so many
algorithms present.
The final research paper will explore a number of the most cutting-edge intrusion
detection techniques and weighs their advantages and disadvantages in an effort to
address this problem.
An evaluation of several ML approaches is also investigated, with four approaches
emerging as the most effective for categorizing intrusions.
ii)Introduction
An intrusion detection system (IDS) is a software program that detects network attacks
by utilizing different machine learning methods.
In the modern technology environment, cyber security continues to be a key area of
concern.
Leveraging extremely sophisticated technology systems largely threatens to expand the
potential for security vulnerabilities.
Numerous intrusion detection techniques currently in use, including firewalls, password
protection, and cryptography, do not ensure overall system security.
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Both enterprises and the governments have discovered the necessity to implement more
sophisticated techniques of intrusion detection across all of their information security
networks.
Even though there are many ways to analyze networks, detect anomalies, and stop
intrusions, machine learning offers some of the best network intrusion detection
solutions.
iii)Techniques for Intrusion Detection
Detection of intrusions using signatures
Identification of intrusions based on anomalies (Machine learning)
Detection of host intrusions
Detection of network intrusions
iv)Flaws on use of Conventional Intrusion Detection Technologies
Need for frequent updating to stay current with new attacks.
A large number of false warnings, which could allow serious dangers to go unnoticed.
Threats based on protocols may cause them to malfunction.
Conventional intrusion detection methods are substantially less successful in detecting
anomalies as a result of network noise.
v)Machine Learning (ML)
Given the serious flaws in the conventional methods, it has become necessary to offer a
more comprehensive approach that operates independently and is more efficient.
Types of Machine Learning Model
Unsupervised machine learning (ML)
Semi-supervised machine learning.
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Supervised machine learning.
vi)Evaluation of Machine Learning Approaches
The research paper will provide a critical evaluation of distinct ways to assist us in
deciding which approach is best in various attack instances.
These methods are outlined in the list below.
Denial-of-Service.
Probing of networks and surveillance.
Improper use of local system privileges.
Remote machine intrusion that is authorized.
vii)An Overview of the Most Effective Methods in ML
Bayes Network.
K-means Clustering.
Multi-layer of defense.
Classifier using random forest.
viii)Advantages of Intrusion Detection Systems Using Machine Learning
Optimization of the network.
Targeted attack source.
Preventing any efforts of intrusion.
Least expensive because private signatures will not necessitate payment.
ix)Summary
Threats are developing quickly in the cyber domain and are challenging to detect using
traditional techniques.
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Machine learning, which is utilized in companies for massive data, is a method for
solving this dilemma.
K-means clustering will be considered as the perfect method for intrusion detection in the
research paper.
One of the efficient strategies for quickly and accurately identifying intrusions is the K-
means clustering algorithm.
x)Conclusion
Various effective approaches are used by machine learning to detect anomalies.
Large businesses and the governments should lead the way in implementing machine learning to
protect their computer networks.
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References
Aljanabi, M., Ismail, M. A., & Ali, A. H. (2021). Intrusion detection systems, issues, challenges,
and needs.
International Journal of Computational Intelligence Systems
,
14
(1), 560-571.
Dini, P., & Saponara, S. (2021). Analysis, design, and comparison of machine-learning
techniques for networking intrusion detection.
Designs
,
5
(1), 9.
McElwee, S. (2017, March). Active learning intrusion detection using k-means clustering
selection. In
SoutheastCon 2017
(pp. 1-7). IEEE.
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